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Documentation Index

Fetch the complete documentation index at: https://docs.raydium.io/llms.txt

Use this file to discover all available pages before exploring further.

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Two distinct concepts

Price impact and slippage are frequently conflated in UIs but refer to different things.
  • Price impact is a deterministic property of a trade against a specific pool state. Given (Δin, reserves), price impact is fully computable before the trade is submitted.
  • Slippage is the realized difference between the price you expected at quote time and the price you actually got at execution time. It is a function of latency, concurrent transactions, and block inclusion order — not of the pool math.
A 1% quote against an otherwise-idle pool has 0% slippage if it lands in the next block; the 1% was the price impact. That same quote lands 0.2% worse if another trade hits the pool first — the extra 0.2% is slippage.

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Formal definitions

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Price impact

p_before = pool.spot_price()
p_after  = pool.spot_price_if_trade(Δin) applied
impact   = (p_before − p_after) / p_before       // can be signed
For a CPMM: impact ≈ 2 · Δin / reserve_in for small trades. For CLMM: depends on how many ticks the trade crosses; often flat within the current tick range, jumping at each tick cross.

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Realized slippage

quoted_out = amount_out computed at quote time
actual_out = amount_out observed on-chain
slippage   = (quoted_out − actual_out) / quoted_out
Slippage is always non-negative (or zero), assuming the quote was honest. A negative value would mean you got more than quoted — possible if the pool state moved in your favor between quote and execution.

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Sizing minAmountOut and maxAmountIn

Every Raydium swap takes a slippage-protection bound:
  • SwapBaseInput(amount_in, min_amount_out) — exact-input, lower-bound the output.
  • SwapBaseOutput(max_amount_in, amount_out) — exact-output, upper-bound the input.
The SDK computes these as: 
const computed = raydium.<pool_type>.computeAmountOut({
  poolInfo,
  amountIn,
  mintIn,
  mintOut,
  slippage: 0.005,     // 0.5% tolerance
});

// computed.amountOut         — the "expected" quote
// computed.minAmountOut      — amountOut × (1 − slippage), used as the on-chain bound
// computed.priceImpact       — deterministic, pool-state-only
// computed.fee               — total fee charged (all components summed)
The slippage tolerance is a buffer around the price impact, not the price impact itself. A 0.5% tolerance means “accept at most 0.5% worse than my quote” — independent of whether the price impact was 0.01% (a tiny trade) or 2% (a large trade). For a 2% price-impact trade with 0.5% tolerance, minAmountOut is 2.5% below the pre-trade spot — the sum of impact and tolerance, essentially. There is no single right number; the right bound depends on:
  1. Pair stability. Stablecoin-stablecoin pools can safely use 0.1%. Volatile meme-pair pools often need 3–5% just to reliably land.
  2. Trade size. Larger trades have larger price impacts, so tolerance needs to scale with them to avoid reversion. The SDK’s auto-slippage defaults around max(0.5%, 2 × price_impact) for this reason.
  3. Block inclusion latency. Transactions that sit in the mempool for multiple blocks are exposed to more concurrent trades. Jito bundles and priority fees reduce this.
Rules of thumb (Raydium UI defaults):
Pair typeDefault tolerance
Stable-stable (USDC-USDT, USDC-USDS)0.1%
Stable-major (USDC-SOL, USDC-BTC)0.5%
Major-major (SOL-BTC, SOL-ETH)1%
Volatile (meme tokens, illiquid long-tail)3–5%

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Differences across AMM types

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CPMM

Price impact is smooth and continuous (closed-form 2 · Δin / reserve_in). Slippage tolerance scales linearly with trade size.

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AMM v4

Same curve math as CPMM, but the “effective reserves” include the pool’s OpenBook-posted limit orders. In practice this means:
  • Quoting off raw vault balances underestimates reserves and therefore overestimates impact.
  • The SDK fetches AmmInfo and sums vault + on_book.free + on_book.locked to get the right number.
  • Stale OpenBook state (crank blocked) can cause the quoted impact to diverge from on-chain reality. Aggregators routinely pre-crank (permissionless MonitorStep) before a large AMM-v4 trade.

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CLMM

Price impact is piecewise. Within the current tick range, impact is approximately linear in Δin / L. Crossing a tick boundary can change L discretely, causing a sudden jump in the marginal price. A trade that crosses several sparsely-populated ticks can have much higher impact than the 2 · Δin / reserve rule of thumb suggests. The SDK’s CLMM quote iterates the swap step deterministically to return an exact expected amountOut, so minAmountOut = amountOut · (1 − slippage) is correct. But the priceImpact return value should be interpreted as “the spread between pre-trade spot and post-trade spot”, which on CLMM can be much larger than the swap’s effective slippage for a user who only cares about amount_out.

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LaunchLab curve

Similar to CPMM but with an asymmetric curve (quadratic or virtual-reserves). Impact grows faster for late buyers as the curve steepens toward graduation. Pre-buyer UIs should warn when a buy is expected to push the curve more than ~5% of quote_reserve_target in one transaction.

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MEV considerations

On Solana, MEV extraction against swaps mostly takes the form of sandwich attacks: a bot places a back-run transaction that trades after yours, plus a front-run that trades before, both at the same slot. Your trade fills at a worse price than it would have absent the sandwich; the back-run captures the difference. Mitigations:
  1. Tight minAmountOut. Aggressive slippage bounds cause the victim transaction to revert if sandwiched heavily, protecting funds (but wasting gas). On Solana this is standard practice — rejection is cheap.
  2. Jito bundles. Submitting through Jito with a bundled tip excludes middlemen from reordering your tx. Bundles land as atomic blocks.
  3. Priority fees. A high priority fee increases the chance your trade lands in the current leader’s block before a sandwicher can react. Less robust than bundles, more standard.
  4. Private RPC. Submitting through a private RPC (or via a validator’s direct endpoint) reduces the window during which a mempool sandwicher can observe your transaction.
Raydium’s SDK does not bundle; integrators typically layer Jito on top. See integration-guides/routing-and-mev for patterns.

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Slippage for multi-hop routes

When a swap routes through multiple pools (e.g. USDC → SOL → RAY), slippage tolerance should be applied per-hop, not just end-to-end: 
// Bad: 0.5% applied at the end only, so any intermediate hop sliding fails the second hop.
const finalMin = finalAmount * (1 - 0.005);

// Better: each hop enforces its own bound.
const hop1Min  = hop1Amount * (1 - 0.005);
const hop2Min  = hop2Amount * (1 - 0.005);
// End-to-end this is tighter (compound), but atomic — if either hop degrades, the tx reverts early.
The SDK’s router applies per-hop bounds automatically when you call raydium.trade.swap. For custom routers, replicate the pattern.

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Reporting to users

Rules-of-thumb for a good swap UI:
  • Display both expected price impact and slippage tolerance separately.
  • Highlight when price impact exceeds ~2% — “high impact” warning.
  • Highlight when price impact exceeds tolerance — the transaction is almost certain to revert.
  • For volatile pairs, offer a “high slippage mode” that relaxes the bound and shows a stronger warning.

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Pointers

Sources:
  • Raydium SDK v2 slippage / impact implementation.
  • Flashbots / Jito on Solana MEV.